Data topic is trending today, and everyone will have their view of it. So, I will write about four types of data professionals. Anyway, maybe it’s not suited to your thinking, you can put your ideas to discuss more.
First, we must know their responsibility and why the company needs them?
Introductions about Types of Data Professionals
What is Data Engineer? And Why we need them:
To support data scientists and business analysts in interpreting, data engineers work in various settings to build systems. These systems will collect, manage, and convert raw data into usable information. Accordingly, their ultimate goal is to make data accessible so that organizations can use it to evaluate and optimize their performance.
Data Scientist:
A Data scientist uses data to understand and explain the phenomena around them and help organizations make better decisions. Besides that, as a data scientist, you can enjoy mental stimulation, analytical gratification, and cutting-edge technology.
ML Engineer:
Machine learning engineers act as critical members of the data science team. Accordingly, their tasks involve researching, building, and designing the artificial intelligence responsible for machine learning. Besides that, they need to maintain and improve existing artificial intelligence systems.
Data Analyst:
A data analyst is responsible for collecting, cleaning, and analyzing data. Based on these analyzes, people can improve their business decisions.
They must effectively communicate their findings to those who will make the decisions. Data analysts typically have a strong background in mathematics and computer science.
What is the value we compare:
ML Ops
MLOps is the short term for Machine Learning Operations. Accordingly, it represents a set of practices that simplify workflow processes and automate machine learning and deep learning deployments. Above all, it accomplishes the deployment and maintenance of models reliably and efficiently for production at a large scale.
Why do MLOps matter?
- MLOps can help ensure the reproducibility and reliability of machine learning models:
Based on valuable practices such as version control and continuous integration, MLOPs can help ML models become reproducible and reliable. - MLOps increases overall production productivity:
Actually, data scientists should use low-code environments to ship an ML model. These environments provide access to organized, focused data sets so that they can reduce the waste of time and funds. - Improving collaboration between data scientists and IT professionals:
This isn’t the first time we’ve touched on this point, but it’s just that important. But by providing a standard set of practices and/or tools, MLOps helps bridge gaps between these two groups. Finally, they can use to work together more effectively. - Improving the monitoring and management of machine learning models:
MLOps practices such as monitoring and alerts can help organizations to monitor the performance of their machine learning models more effectively and to identify and resolve any issues that arise. - Increasing sales for new products by producing machine learning models:
Based on past purchases and other factors, machine-learning models can recommend personalization. As a result, that’s helpful for sales and marketing teams to identify new opportunities and upsell existing customers.
Data Principles
Data principles set a clear standard that promotes public trust in our data handling and provides high-quality, inclusive, and trusted statistics. So, we can say that High-level Data Principles underpin our Data Strategy.
Data Viz (Data visualization)
Data visualization is data representation using standard graphics, such as charts, plots, infographics, and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.
Storytelling
People value the information data may hold and appreciate the value of stories. Data storytelling is the art of building a narrative around the insights provided by data. It combines the intellectual and emotional in a meaningful and memorable way, leading to action for individuals or organizations.
Business Insight
A business insight combines data and analysis to make sense of and deepen your understanding of a situation, giving your company a competitive edge.
Experimentation
A measurement, test method, experimental design, or quasi-experimental design produces experimental data in science and engineering. In clinical research, any data produced result from a clinical trial.
Stats
Statistics is a set of mathematical methods and tools that enable us to answer important questions about data. It is divided into two categories:
- Descriptive Statistics: This offers methods to summarise data by transforming raw observations into meaningful information that is easy to interpret and share.
- Inferential Statistics: This offers methods to study experiments done on small data samples and chalk out the inferences to the entire population (entire domain).
Now, statistics and machine learning are two closely related areas of study. Statistics is an essential prerequisite for applied machine learning, as it helps us select, evaluate and interpret predictive models.
ML Models
A machine learning model is a program that can find patterns or make decisions from a previously unseen dataset. For example, in natural language processing, machine learning models can parse and correctly recognize the intent behind previously unheard sentences or combinations of words. In image recognition, a machine-learning model can be taught to recognize objects like cars or dogs.
A machine learning model can perform such tasks by having it ‘trained’ with a large dataset. Depending on the task, the machine learning algorithm is optimized during training to find specific patterns or outputs from the dataset. The output of this process – often a computer program with specific rules and data structures – is called a machine learning model.
Summary
So my summary is 4 Types of Data Professionals are equally crucial in the team. Maybe your company is small; you only need 2 types, 1 from the business (Data Analyst) and 1 from the engineer (Data Engineer), to know the primary issue of your Company.
And then, when your company wants to grow in the best way, we will need more 2 roles: ML Engineer and Data Scientist.
This is only my idea, and I get info from my friends who work in 4 roles in “coffee talking time”.
Thank you all for reading my thinking.
References
